High-speed, multi-bearing machines, such as those used in oil refineries, oil extraction platforms, power generation stations, and the like, may include rotating elements like rotors, shafts, and bearings. Generally, these rotating elements rotate at high speeds and may cause vibrations in the machine. There is a defined threshold level to which the variations in vibrations are acceptable. However, if the vibrations increase beyond the threshold level, the machine may be susceptible to various faults, such as machine imbalance, machine misalignment, machine bearing failure, machine bearing instability, machine thrust bearing failure, machine rub, shaft imbalance, shaft crack, machine mounting anomaly, or fluid induced instability. These various faults may cause temporary or permanent damage to the machine. Thus, to prevent occurring of these faults, the various physical quantities related to the machine, such as vibrations may be monitored in real time or near-real time, to identify the occurrence of a fault and/or to determine a fault type. Various techniques have been proposed to monitor the machine in real time or near-real time to determine the occurrence and/or identify which faults may be occurring in the machine.
A conventional technique for real time or near-real time monitoring of a machine and to identify and/or determine faults is provided by a system which includes multiple sensors, a monitoring rack, and a personal computer (“PC”). The multiple sensors are placed in the vicinity of the high speed rotating elements of the machine. These sensors sense the various physical quantities, such as vibrations, occurring in the high speed rotating elements. These sensors generate the sensed data based on the measurements of various such physical quantities and send the sensed data to the monitoring rack, which aggregates and conditions the sensed data. The monitor rack further generates alarms to protect the machine from damage. The monitor rack then sends the aggregated data to the PC for determination of the faults in the machine using software-based analysis.
However, the above-described conventional system requires a large amount of hardware to monitor the machine in real time or near-real time. Moreover, high costs are generally involved in installing, calibrating, operating, and maintaining the monitor rack, the PC, and the analysis software. Thus, it is desirable to minimize the hardware requirements and the associated cost of determining the fault in the machine.
Accordingly, there is a need for a system that provides monitoring of the machine and determination of faults with minimized hardware requirements. There is a further need for systems and methods for sensor-level machine monitoring.